Dear all,

does anybody knows whether the functions kernelUD or kernel.area 
{adehabitatHR} have some kind of bug in the calculation of the kde100?

I run the following script:

### Calculate KDE95 by animal and Export Polygons to shapefile

library(adehabitatHR)

all.xy.proj<-all.locs.proj[,c("X.CRP","Y.CRP")]
ind.id<-as.data.frame(all.locs.proj[,"individual.local.identifier"])
all.xy.df<-SpatialPointsDataFrame(all.xy.proj,data=ind.id,proj4string=CRS(CRP.PROJ.4))

all.kde.proj<-kernelUD(all.xy.df,h="href",grid=40,same4all=F,kern="bivnorm",extent=0.5)
all.kde.pol<-getverticeshr(all.kde.proj,percent=95,unin="m",unout="km2")
writeOGR(obj=all.kde.pol,dsn=paste(outputdir,"KDE95",sep=""),layer="all_kde95",driver="ESRI 
Shapefile")

  # Calculate Individual KDE Areas

all.kde.areas<-kernel.area(all.kde.proj,percent=seq(50,100,by=5),unin="m",unout="km2")
write.table(all.kde.areas, paste(outputdir, "all_kdelist.txt", sep=""), 
sep="\t")

The result is 95% fine. The values are plausible till the kde95 then 
they increase dramatically up to factor 11.3 (e.g. kde95=350 sqKm, 
kde100=3968 sqKm).
First I thought to outliers (e.g. sporadic exploration out of the 
territory, etc.) that might increase the kde100 value so I checked the 
mcp100. This latter is much smaller than the kde100 (mcp100=527 sqKm). 
These values are extreme but the average increase between kde95 and 
kde100 across my dataset is anyway factor 7.6!

Does anybody have an explanation of this fact?

Thanks in advance.

Ivan Palmegiani

P.S. I'm not using KDE100 for my analysis, it's just out of curiosity!


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